Comparison of sixteen methods for fusion of data from impulse-radar sensors and depth sensors applied for monitoring of elderly persons

Abstract This paper is devoted to the comparison of sixteen methods for fusion of measurement data from impulse-radar sensors and infrared depth sensors, i.e. two sensor technologies that may be employed in care services for elderly persons. These methods are compared with respect to their potential for decreasing the uncertainty of estimation of monitored person’s position: eight of them consist in fusing the impulse-radar data and depth data whenever new data points are available, and the other eight consist in fusing the whole sequences of the data acquired during a predefined time interval. The numerical experiments, based on the real-world data, show that the best overall results are obtained for two methods of data fusion, viz. a method based on the Kalman filter and a method using the Tikhonov regularization technique to generate a smooth approximation of the data.

[1]  Chandra Sekhar Seelamantula,et al.  On the Selection of Optimum Savitzky-Golay Filters , 2013, IEEE Transactions on Signal Processing.

[2]  Antonio Lázaro,et al.  Techniques for Clutter Suppression in the Presence of Body Movements during the Detection of Respiratory Activity through UWB Radars , 2014, Sensors.

[3]  K. C. Chou,et al.  Multiscale recursive estimation, data fusion, and regularization , 1994, IEEE Trans. Autom. Control..

[4]  Jonathan J. Stickel,et al.  Data smoothing and numerical differentiation by a regularization method , 2010, Comput. Chem. Eng..

[5]  Guilherme A. S. Pereira,et al.  A Probabilistic Approach for Fusing People Detectors , 2015 .

[6]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[7]  Kristin Taraldsen,et al.  Identification of gait domains and key gait variables following hip fracture , 2015, BMC Geriatrics.

[8]  Fakhri Karray,et al.  Multisensor data fusion: A review of the state-of-the-art , 2013, Inf. Fusion.

[9]  H. Späth.  Mathematical algorithms for linear regression , 1991 .

[10]  Sabine Van Huffel,et al.  Overview of total least-squares methods , 2007, Signal Process..

[11]  Angelo M. Sabatini,et al.  Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking , 2016, Sensors.

[12]  James Llinas,et al.  Multisensor Data Fusion , 1990 .

[13]  Jun Miura,et al.  Specific person tracking using 3D LIDAR and ESPAR antenna for mobile service robots , 2015, Adv. Robotics.

[14]  Roman Z. Morawski,et al.  Comparative study of three algorithms for estimation of echo parameters in UWB radar module for monitoring of human movements , 2016 .

[15]  L. Reichel,et al.  A new framework for multi-parameter regularization , 2016 .

[16]  Juan José Pantrigo,et al.  Real-time human body tracking based on data fusion from multiple RGB-D sensors , 2017, Multimedia Tools and Applications.

[17]  Emmanuel Andrès,et al.  From Fall Detection to Fall Prevention: A Generic Classification of Fall-Related Systems , 2017, IEEE Sensors Journal.

[18]  Nasser Kehtarnavaz,et al.  A survey of depth and inertial sensor fusion for human action recognition , 2015, Multimedia Tools and Applications.

[19]  Marjorie Skubic,et al.  Fall Detection in Homes of Older Adults Using the Microsoft Kinect , 2015, IEEE Journal of Biomedical and Health Informatics.

[20]  Moe Z. Win,et al.  Sensor Radar Networks for Indoor Tracking , 2014, IEEE Wireless Communications Letters.

[21]  Behzad Moshiri,et al.  Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach , 2017, Sensors.

[22]  Jun Jason Zhang,et al.  Ultra-wideband radar-based accurate motion measuring: human body landmark detection and tracking with biomechanical constraints , 2015 .

[23]  Michelle M. Lusardi,et al.  Is Walking Speed a Vital Sign? Absolutely! , 2012 .

[24]  Helge Toutenburg,et al.  Linear models : least squares and alternatives , 1999 .

[25]  Roman Z. Morawski,et al.  Development of software application dedicated to impulse- radar-based system for monitoring of human movements , 2016 .

[26]  Bart Vanrumste,et al.  Fall prevention and detection , 2016 .

[27]  Lieven De Lathauwer,et al.  Structured Data Fusion , 2015, IEEE Journal of Selected Topics in Signal Processing.

[28]  William Holderbaum,et al.  Application of data fusion techniques and technologies for wearable health monitoring. , 2017, Medical engineering & physics.

[29]  Hassan Ghasemzadeh,et al.  Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges , 2017, Inf. Fusion.

[30]  James McNames,et al.  Inertial and time-of-arrival ranging sensor fusion. , 2017, Gait & posture.

[31]  Shie-Jue Lee,et al.  A multiple-kernel support vector regression approach for stock market price forecasting , 2011, Expert Syst. Appl..

[32]  Marjorie Skubic,et al.  Doppler Radar Fall Activity Detection Using the Wavelet Transform , 2015, IEEE Transactions on Biomedical Engineering.

[33]  Arthur C. Sanderson,et al.  Sensor fusion for occupancy detection and activity recognition using time-of-flight sensors , 2016, 2016 19th International Conference on Information Fusion (FUSION).

[34]  Hugh F. Durrant-Whyte,et al.  Multisensor Data Fusion , 2016, Springer Handbook of Robotics, 2nd Ed..

[35]  José Jaime da Cruz,et al.  Fuzzy adaptive extended Kalman filter for UAV INS/GPS data fusion , 2016 .

[36]  Ioannis Paraskevopoulos,et al.  Fall prevention intervention technologies: A conceptual framework and survey of the state of the art , 2016, J. Biomed. Informatics.

[37]  Jitendra R Raol,et al.  Data Fusion Mathematics: Theory and Practice , 2015 .

[38]  Bruno Ando,et al.  A Multisensor Data-Fusion Approach for ADL and Fall classification , 2016, IEEE Transactions on Instrumentation and Measurement.

[39]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[40]  Sameer Singh,et al.  Approaches to Multisensor Data Fusion in Target Tracking: A Survey , 2006, IEEE Transactions on Knowledge and Data Engineering.

[41]  Raghad Al-Husari,et al.  Precision landing using an adaptive fuzzy multi-sensor data fusion architecture , 2018, Appl. Soft Comput..

[42]  Michio Sugeno,et al.  Industrial Applications of Fuzzy Control , 1985 .

[43]  Chouireb Fatima,et al.  Data fusion strategy for the navigation of a mobile robot in an unknown environment using fuzzy logic control , 2017, 2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B).

[44]  Yalou Huang,et al.  Multisensor Information Fusion for People Tracking With a Mobile Robot: A Particle Filtering Approach , 2015, IEEE Transactions on Instrumentation and Measurement.

[45]  Roman Z. Morawski,et al.  Comparison of two techniques for monitoring of human movements , 2017 .

[46]  Xiaoli Meng,et al.  Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking , 2017, Sensors.

[47]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[48]  Zbigniew Szymanski,et al.  Deep learning classifier for fall detection based on IR distance sensor data , 2015, 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).

[49]  Agathoniki Trigoni,et al.  Fusion of Radio and Camera Sensor Data for Accurate Indoor Positioning , 2014, 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems.

[50]  Marco Mercuri,et al.  Embedded DSP-Based Telehealth Radar System for Remote In-Door Fall Detection , 2015, IEEE Journal of Biomedical and Health Informatics.

[51]  Il Hong Suh,et al.  Multiple Kinect Sensor Fusion for Human Skeleton Tracking Using Kalman Filtering , 2016 .

[52]  Ahmad Diab,et al.  Elder Tracking and Fall Detection System Using Smart Tiles , 2017, IEEE Sensors Journal.

[53]  J. Ali.  Strapdown inertial navigation system/astronavigation system data synthesis using innovation-based fuzzy adaptive Kalman filtering , 2010 .

[54]  Jia Li,et al.  Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device , 2015, Sensors.

[55]  O. Celik,et al.  Systematic review of Kinect applications in elderly care and stroke rehabilitation , 2014, Journal of NeuroEngineering and Rehabilitation.

[56]  R. Herrmann,et al.  M-sequence-based ultra-wideband sensor network for vitality monitoring of elders at home , 2015 .

[57]  Luming Zhang,et al.  Fusion of Magnetic and Visual Sensors for Indoor Localization: Infrastructure-Free and More Effective , 2017, IEEE Transactions on Multimedia.

[58]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

[59]  Paolo Bernardi,et al.  Design, Realization, and Test of a UWB Radar Sensor for Breath Activity Monitoring , 2014, IEEE Sensors Journal.

[60]  Roman Z. Morawski,et al.  Use of kinematic and mel-cepstrum-related features for fall detection based on data from infrared depth sensors , 2018, Biomed. Signal Process. Control..

[61]  Wenbing Tao,et al.  Radar-based fall detection based on Doppler time-frequency signatures for assisted living , 2015 .

[62]  Marjorie Skubic,et al.  Average in-home gait speed: investigation of a new metric for mobility and fall risk assessment of elders. , 2015, Gait & posture.

[63]  H. Vincent Poor,et al.  Position Estimation via Ultra-Wide-Band Signals , 2008, Proceedings of the IEEE.

[64]  Roman Z. Morawski,et al.  Monitoring of human movements by means of impulse-radar sensors , 2015 .

[65]  Carl de Boor,et al.  A Practical Guide to Splines , 1978, Applied Mathematical Sciences.

[66]  Ethem Alpaydin,et al.  Multiple Kernel Learning Algorithms , 2011, J. Mach. Learn. Res..

[67]  Nasser Kehtarnavaz,et al.  Improving Human Action Recognition Using Fusion of Depth Camera and Inertial Sensors , 2015, IEEE Transactions on Human-Machine Systems.

[68]  Mongi A. Abidi,et al.  Data fusion: color edge detection and surface reconstruction through regularization , 1996, IEEE Trans. Ind. Electron..

[69]  Tomaso A. Poggio,et al.  Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..

[70]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[71]  Baoju Zhang,et al.  Experimental study of through-wall human detection using ultra wideband radar sensors , 2014 .

[72]  Leszek Podsedkowski,et al.  Data Fusion Using Fuzzy Logic Techniques Supported by Modified Weighting Factors (FLMW) , 2016, Int. J. Fuzzy Syst..

[73]  Bart Vanrumste,et al.  Developing a system that can automatically detect health changes using transfer times of older adults , 2016, BMC Medical Research Methodology.

[74]  Yee-Ming Chen,et al.  Fuzzy logic approach to multisensor data association , 2000 .